Automatic recognition based on image fusion techniques are widely used to integrate a lower spatial resolution multispectral image with a higher spatial resolution panchromatic image. The earthquake events were first ...
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ISBN:
(纸本)9780819469502
Automatic recognition based on image fusion techniques are widely used to integrate a lower spatial resolution multispectral image with a higher spatial resolution panchromatic image. The earthquake events were first researched after the Kocaeli Earthquake of 1999 show that the spatial images from various satellites could be exploited. The remotesensing which in terms of spatial resolution and data processing open new possibilities concerning the natural hazard assessment. However, the existing techniques either cannot avoid distorting the image spectral properties or involve complicated and time-consuming frequency decomposition and re-construction processing. To address these problems, we present our study on a HIS transform and intensity modulation algorithm. The algorithm is further optimized a proposed objective of minimizing error rate. Experiments in recognition of building damage due to earthquakes applications show that the algorithm provides better recognition accuracy than others. Although some environment problems, such as the influence of sunshine need further research, the proposed method can benefit further study of the application.
This study extended the computation of GLCM (gray level co-occurrence matrix) to a three-dimensional form. The objective was to treat hyperspectral image cubes as volumetric data sets and use the developed 3D GLCM com...
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ISBN:
(纸本)9783540741954
This study extended the computation of GLCM (gray level co-occurrence matrix) to a three-dimensional form. The objective was to treat hyperspectral image cubes as volumetric data sets and use the developed 3D GLCM computation algorithm to extract discriminant volumetric texture features for classification. As the kernel size of the moving box is the most important factor for the computation of GLCM-based texture descriptors, a three-dimensional semi-variance analysis algorithm was also developed to determine appropriate moving box sizes for 3D computation of GLCM from different data sets. The developed algorithms were applied to a series of classifications of two remotesensing hyperspectral image cubes and comparing their performance with conventional GLCM textural classifications. Evaluations of the classification results indicated that the developed semi-variance analysis was effective in determining the best kernel size for computing GLCM. It was also demonstrated that textures derived from 3D computation of GLCM produced better classification results than 2D textures.
The blind image restoration based on the nonlinear inverse heat diffusion equation and total variation model has been developed in the past decades. In this paper, a novel genetic algorithm by combining genetic algori...
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ISBN:
(纸本)9780819469540
The blind image restoration based on the nonlinear inverse heat diffusion equation and total variation model has been developed in the past decades. In this paper, a novel genetic algorithm by combining genetic algorithm with anisotropic diffusion technique is put forward to implement the super-resolution image restoration. In the procedure of mutation, we proposed a new genetic mutation operator based on anisotropic diffusion to guide the mutation. The solution space with the higher resolution is formed by the introduction of the new genetic mutation operator. The validity and robustness of our method was demonstrated by using aerial images.
As one of the popular and advanced statistical learning algorithms, Support Vector Machine (SVM) has been the new hot study area of patternrecognition and machine learning in recent years. SVM has such advantages as ...
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ISBN:
(纸本)0769528740
As one of the popular and advanced statistical learning algorithms, Support Vector Machine (SVM) has been the new hot study area of patternrecognition and machine learning in recent years. SVM has such advantages as suitableness to high dimensional data, requirement of few samples and robustness to uncertainty, so it can be used to hyperspectral remotesensingimage classification effectively. Based on the theory of SVM, a new approach for information classification on hyperspectral sensor has been developed by the experimental case of spatial information classification in central area of Shanghai city with PHI image. The algorithm is synthetically compared with the traditional classification methods. The experiment results confirm the effectiveness of the proposed method, which results in higher classification accuracy than the traditional methods.
Pixel-level image fusion, as an important part of image fusion algorithms, con combine spectral information of coarse resolution imagery with finer spatial resolution imagery. In this paper we propose a new pixel-leve...
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ISBN:
(纸本)9781424410651
Pixel-level image fusion, as an important part of image fusion algorithms, con combine spectral information of coarse resolution imagery with finer spatial resolution imagery. In this paper we propose a new pixel-level multisensor image fusion algorithm for sharpening multispectral (MS) data with better spectral preservation ability and higher spatial resolution combining intensity-hue-saturation (IHS) transformation and discrete wavelet transform (DWT) and develop a fusion rule based 017 structure information extracted from the high spatial resolution image. Experimental results are presented on a set of MS images and a Systeme Pour l'Observation de la Terre Panchromatic image. The proposed method was compared visually and qualilatively with existing standard IHS transformation, standard DWT strategies and a simple combinationon of IHS and DWT Comparative analysis shows that the proposed algorithm has higher fidelity to the Original spectral information and better spatial quality with respect to those methods for the test images.
In this paper, welding pool imageprocessing software is developed to measure the weld shape parameters in different images. Firstly the visual sensing system was established according to the principle of the passive ...
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ISBN:
(纸本)9783540733737
In this paper, welding pool imageprocessing software is developed to measure the weld shape parameters in different images. Firstly the visual sensing system was established according to the principle of the passive visual imagesensing. Next, the imageprocessing and patternrecognition techniques are discussed to get a clear contour of the pool and measure its size. Finally, in order to testify the efficiency of the software, an image of aluminum alloy welding pool is used and validating results indicate these techniques can be applied practically.
In this keynote address, we address three-dimensional (3D) distortion-tolerant object recognition using photon-counting integral imaging (II). A photon-counting linear discriminant analysis (LDA) is discussed for clas...
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ISBN:
(纸本)9780819466884
In this keynote address, we address three-dimensional (3D) distortion-tolerant object recognition using photon-counting integral imaging (II). A photon-counting linear discriminant analysis (LDA) is discussed for classification of photon-limited images. We develop a compact distortion-tolerant recognition system based on the multiple-perspective imaging of II. Experimental and simulation results have shown that a low level of photons is sufficient to classify out-of-plane rotated objects.
A context-sensitive change-detection technique based on semi-superv-ised learning with multilayer perceptron is proposed. In order to take contextual information into account, input patterns are generated considering ...
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ISBN:
(纸本)9783540770459
A context-sensitive change-detection technique based on semi-superv-ised learning with multilayer perceptron is proposed. In order to take contextual information into account, input patterns are generated considering each pixel of the difference image along with its neighbors. A heuristic technique is suggested to identify a few initial labeled patterns without using ground truth information. The network is initially trained using these labeled data. The unlabeled patterns are iteratively processed by the already trained perceptron to obtain a soft class label. Experimental results, carried out on two multispectral and multitemporal remotesensingimages, confirm the effectiveness of the proposed approach.
Optical registration requires the same spatial coordinate of all the corresponding pixels in the multi-spectrum images. The imaging equation of the multi-lens camera is established in the same ground coordinate. Some ...
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ISBN:
(纸本)9780819469502
Optical registration requires the same spatial coordinate of all the corresponding pixels in the multi-spectrum images. The imaging equation of the multi-lens camera is established in the same ground coordinate. Some factors are analyzed and simulated with the actual parameters of a multi-spectral camera. The ground coordinate differences are compared among these factors, and the results are shown in paper. The shutter delay between the camera shutter and POS should be considered firstly, and the influence by focal length is more marked than the influence by angle of optical axis. The multi-spectral cameras should be selected to keep higher shutter synchronism.
Hyperspectral RS technology organically combines the radiation information and the space information. The spectrum information, which the hyperspectral image enriches, can be better to carry on the ground target class...
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ISBN:
(纸本)9780819469526
Hyperspectral RS technology organically combines the radiation information and the space information. The spectrum information, which the hyperspectral image enriches, can be better to carry on the ground target classification, compare with panchromatic remotesensingimage and multispectral remotesensingimage. As support vector machine was applied to many fields successfully recent years, using kernel methods, the classic linear methods can cope with the nonlinear problem, which was called the 3rd revolution of pattern analysis algorithms. This paper introduced two classifying methods for hyperspectral image based on kernel function, Support Vector Machine and Kernel Fisher Discriminant Analysis, and studied the selection of kernel function and its parameters as well as multi-class decomposition. We use radial basic function kernel, one against one or one against rest decomposition methods to construct multi-class classifier, and optimize parameter selection using cross-validating grid search to build an effective and robust kernel classifier. It is verified that, through the OMIS and AVIRIS image classifying experiments, comparing with common image classifying methods, kernel classifying method can avoid Hughes phenomenon, thus improve the classifying accuracy.
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